| Budget Amount *help |
¥18,200,000 (Direct Cost: ¥14,000,000、Indirect Cost: ¥4,200,000)
Fiscal Year 2019: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
Fiscal Year 2017: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
|
|---|
| Outline of Final Research Achievements |
This research supports the enhancement of the intelligent transport system through the loosely-coupled cooperation of roadside units and on-board units. For this purpose, a methodology of distributed deep-neural network-based learning for decision-making and status prediction of the transport system is developed. Specifically, a method that allows multiple devices to share the functions of a deep neural network and cooperate with each other, as well as a distributed event processing platform that has a mechanism to perform load balancing and cooperative processing in a loosely coupled manner, have been investigated. Besides, a vehicle mobility reproduction platform to evaluate these mechanisms are developed. Through proof-of-concept implementations and numerous simulation experiments, the feasibility of the proposed methodologies is shown.
|
